Research Bits: August 19


Co-packaged optics Researchers from the Massachusetts Institute of Technology (MIT) and Bridgewater State University developed a new way to co-package photonic and electronic chips that uses existing automated pick-and-place assembly equipment in traditional fabs along with a less-expensive passive alignment process. “We’ve developed a packaging design [for integrating photonics with el... » read more

System-Level Design For 1.6 Tbps Interoperability In AI Data Centers


By Madhumita Sanyal and Diwakar Kumaraswamy The rapid escalation of AI/ML workloads—driven by increasingly large language models—is reshaping high-performance computing and AI data center architectures. Real-time inference and large-scale training are pushing the limits of compute and interconnect performance. With model sizes and parameter counts doubling every 4–6 months, infrastruct... » read more

Re-Architecting AI For Power


The industry is becoming increasingly concerned about the amount of power being consumed by AI, but there is no simple solution to the problem. It requires a deep understanding of the application, the software and hardware architectures at both the semiconductor and system levels, and how all of this is designed and implemented. Each piece plays a role in the total power consumed and the utilit... » read more

Moving Past “It Works” — Intelligent Optimization Is the Key to PCB Excellence


In the fast-evolving field of electronic systems design, engineers are under increasing pressure to deliver innovative, high-performance products within ever-shrinking development cycles. Traditional methods—relying on intuition, trial-and-error testing, and even basic simulation—struggle to keep pace with the growing complexity of modern systems. Nowhere is this more evident than in printe... » read more

Maximize Uptime And Improve TCO: RAS And Telemetry In HBM4 For Data Centers


As AI workloads scale and data center operations become increasingly complex, it is critical to keep the infrastructure up and running. Total Cost of Ownership (TCO) is a key metric that includes not only the upfront cost of hardware but also the ongoing expenses of power, cooling, maintenance, and—most importantly—downtime. A single memory failure in a hyperscale AI cluster can cascade int... » read more

UEC-CBFC: Credit-Based Flow Control For Next-Gen Ethernet In AI And HPC


For ages, Ethernet has been the backbone of networking — starting from simple web browsing to cloud computing, data centers, automobiles, and more. Ethernet has enabled countless innovations, and now, it's expanding to meet the demands of AI and HPC. As the world shifts toward these new technologies, new challenges are emerging. These include increased scale, higher bandwidth density, mult... » read more

Reliable Training Data Paramount To AI Model Success


AI systems are increasingly being integrated into safety- and mission-critical applications ranging from automotive to health care and industrial IoT, stepping up the need for training data that is reliable, secure, and which is generated from trusted sources. AI activity is growing exponentially, as everybody tries to figure out how to apply it to their domain, application, or workload. In ... » read more

Digital Engineering Transforms Chips For The Future


The semiconductor industry stands at a critical turning point. With global semiconductor sales exceeding $600 billion last year, the need for the industry to scale has never been more apparent. As AI applications drive unprecedented requirements for processing capabilities, chip designers are turning to advanced simulation technologies to enable the digital engineering workflows that will sup... » read more

Will New Processor Architectures Raise Energy Efficiency?


Data centers continue to heat up as new processors consume more energy than ever before. Cooling is the primary weapon against the heat these processors generate, but it won’t be able to keep up forever with traditional processor architectures. New ones may be necessary. There are opportunities today to make well-known architectures more energy-efficient, but the number of options for subs... » read more

Start Experimenting With Neural Super Sampling For Mobile Graphics


Mobile game developers around the world face increasing pressure to meet user expectations for sharper visuals, smoother gameplay, and longer battery life. Balancing these goals on constrained mobile devices often means making trade-offs. Traditional upscaling methods offer limited flexibility. Real-time AI rendering remains complex, power-hungry, or hardware dependent. Neural Super Sampling... » read more

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